Project 17: Logistic Regression | Hyperparameter tuning | EDA
Hyperparameter tuning in machine learning refers to the process of finding the best set of hyperparameters for a model to optimize its performance. Hyperparameters are configuration settings external to the model that cannot be learned from the data. In the context of logistic regression, hyperparameters typically include parameters like the learning rate, regularization strength, and the type of regularization (L1 or L2). Tuning these hyperparameters is crucial for achieving the best possible predictive performance of the logistic regression model.
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